Regulation, investment, and growth across countries.

• Author: Dawson, John W.

Numerous studies have explored the relationship between economic freedom and long-run economic growth across countries. (1) One particular aspect of economic freedom that has received relatively little attention in the empirical growth literature, however, is the extent of government regulation. Determining the impact of regulation on cross-country economic performance has been virtually impossible because of the inherent difficulties in measuring the scope of regulation across countries. While a few studies investigate various aspects of specific regulations, none are able to assess the importance of a comprehensive measure of regulation on long-run economic performance in a large sample of countries. (2)

Fortunately, the recent availability of data on the scope of regulation across countries now makes such a study possible. Recent releases of the Fraser Institute's Economic Freedom of the World annual report include data on the regulatory environment in a large number of countries. Beginning with the 2002 release, the report's economic freedom of the world (EFW) index includes regulation as one of its five major areas. (3) Within this area of the index, there are as many as 15 components covering credit market, labor market, and business regulations for which cross-country data are available. Table 1 lists these underlying components as found in Gwartney and Lawson (2005). (4)

There are no well-developed theories of how regulation should affect long-run economic performance. However, it is reasonable to think that regulation may affect an economic agent's ability to engage in voluntary exchange and the efficiency with which resources are used in an economy. Thus, we might expect that the level of regulation in a country is related to indicators of long-run economic performance such as the level of investment or per capita income growth. The line of reasoning here is similar to that relating the more broadly defined concept of economic freedom to long-run performance. Note that this discussion does not presume that the effect of regulation is either positive or negative--this is ultimately an empirical issue. As such, the analysis below will follow the same approach as has been used in the empirical growth literature where the impact of economic freedom has been addressed. The results will show whether or not regulation has economic implications beyond those related to economic freedom more generally.

This article uses data on regulation from the Gwartney and Lawson (2005) EFW index to investigate the impact of regulation on long-run economic performance across countries. The underlying data allow the analysis to include a broad measure of regulation across countries, as well as the more narrowly focused areas of credit market, labor market, and business regulation.

Empirical Model, Methodology, and Data

Model and Specification Issues

The cross-country empirical specification used to estimate the relationship between regulation and growth is an extension of the Solow (1956) model. (5) The estimating equation can be written as:

(1) [g.sub.Y] = [a.sub.0] + [a.sub.1][y.sub.0] + [a.sub.2][s.sub.K] + [a.sub.3][g.sub.LF] + [a.sub.4]I + e,

where [g.sub.Y] is the cumulative growth rate of real per capita GDP, [y.sub.0] is a measure of the initial income level, [s.sub.K] is the share of gross domestic investment in GDP, [g.sub.LF] is the growth rate of the labor force, I is an index of government institutions, and e is an error term. (6) All explanatory variables are measured as annual averages over the sample period and are entered as natural logarithms unless noted otherwise.

Many cross-country growth studies also include a measure of human capital in specifications similar to equation (1). Such measures, however, were generally found to be statistically insignificant in the analysis below, and so were excluded from the model. (7) Some recent studies have also included cross-country geographic measures such as tropical location and distance to major markets (see, e.g., Gwartney, Lawson, and Holcombe 2004, 2006). Such measures were generally found to be statistically insignificant in the analysis below, and so were excluded from the model. (8)

While growth theory is somewhat precise in terms of modeling the economic determinants of growth, it is much less instructive in modeling the role of institutions. As a result, existing studies rely on various ad hoe specifications in their empirical analysis. More specifically, it is not clear whether the level or changes in institutions, or both, should be included in the model. A number of early studies included both levels and changes in economic freedom (see, e.g., Dawson 1998 and Gwartney, Lawson, and Holcombe 1999). Subsequently, however, de Haan and Sturm (2000) found in their sensitivity analysis that changes in economic freedom (and not levels) are robustly related to growth. In my analysis, I use the following empirical specifications:

(1A) [g.sub.Y] = [a.sub.0] + [a.sub.1][y.sub.0] + [a.sub.2][s.sub.K] + [a.sub.3][g.sub.LF] + [a.sub.4]R + e,

(1B) [g.sub.Y] = [a.sub.0] + [a.sub.1][y.sub.0] + [a.sub.2][s.sub.K] + [a.sub.3][g.sub.LF] + [a.sub.4]R + [a.sub.5ΔEF + [a.sub.6]EF + e,

(1C) [g.sub.Y] = [a.sub.0] + [a.sub.1][y.sub.0] + [a.sub.2][s.sub.K] + [a.sub.3][g.sub.LF] + [a.sub.4]R + [a.sub.5]ΔEF + e,

where R and EF are indexes of regulation and economic freedom, respectively. In each specification, the level of regulation R is included to examine the effect of regulation on cross-country growth. Since regulation has tended to change relatively slowly over the sample period, there is more variation in the level of regulation (R) across countries than in the change in regulation (ΔR). Thus, R is more likely to explain differences in growth rates across countries.

Specification (1A) provides an estimate of regulation's influence on growth without controlling for differences in economic freedom across countries. Specification (1B) includes both the level and change in economic freedom (both EF and ΔEF) whereas (1C) includes only the change in economic freedom (ΔEF). The choice between specifications (1B) and (1C) is a difficult one. There are potential advantages and disadvantages with each specification. The use of (1B) maintains comparability with many of the early benchmark studies that include economic freedom in a cross-country growth analysis. However, including both levels and changes in economic freedom is equivalent to including levels at various points in time over the sample period. Using such a specification increases the chances of finding a spurious relationship that results from reverse causation between the variables of interest (see Dawson 2003: 481-82). In addition, the Spearman rank correlation between the indexes of economic freedom and regulation is 0.72 (with a p-value

Rather than engage in a philosophical debate on which specification is correct, I will discuss the results from all three specifications. This approach should be sufficient for determining the extent to which regulation can explain cross-country differences in growth rates beyond that explained by economic freedom in general.

Methodology

The Solow model relates growth in income to the evolution of the labor force, capital stock, and technology over time. While this approach shows the importance of these factors in determining long-run growth rates, it does not explain why these factors themselves vary across countries. Thus, a secondary analysis that has become standard in the empirical growth literature is to discern the influence of institutions (such as economic freedom and regulation) on growth and the factors that contribute to growth (such as investment). Such an analysis amounts to determining the "direct" and "indirect" effects of institutions. The direct effect refers to the effect of institutions on growth through total factor productivity, while the indirect effect refers to the effect of institutions on investment that, in turn, influences growth. Dawson (1998), for example, found that economic freedom has a positive impact on growth through both channels.

We will investigate the alternative channels through which regulation might affect long-run growth. Although various approaches have been suggested for distinguishing between the direct and indirect effects of a variable of interest, I follow the approach recently suggested by Gwartney, Lawson, and Holcombe (2004, 2006). Their approach, as applied to the specification in (1A), is to first estimate a cross-country investment equation of the form:

(2) [s.sub.K] = [b.sub.0] + [b.sub.1][y.sub.0] + [b.sub.2][g.sub.LF] + [b.sub.3] R + v.

Note that (2) includes all explanatory variables from (1A), except [s.sub.K], to explain the variation in [s.sub.K] across countries. Thus, (2) estimates the influence of factors such as regulation on investment rates, even after other factors that might be related to investment, such as initial income and labor force growth, are accounted for. The estimated residual from (2), [??], on the other hand, represents that part of the investment rate that is not related to regulation and the other explanatory variables in (1A).

Next, Gwartney, Lawson, and Holcombe (2004, 2006) suggest re-estimating equation (1A) with the residuals, [??], from equation (2) substituted for [s.sub.K]:

(3) [g.sub.Y] = [c.sub.0] + [c.sub.1][y.sub.0] + [c.sub.2][??] + [c.sub.3][g.sub.LF] + [C.sub.4]R + u.

The logic of this approach is to recognize that the estimated coefficient [[??].sub.4] from (1A) reflects the impact of a change in R after the effects of other variables, including [s.sub.K], are accounted for. As such, [[??].sub.4] reflects only the direct effect of R on growth that results from its impact on total factor productivity. But, of course, this is only part of the impact of regulation on growth. In the estimation of (3), on the other hand, the estimated coefficient for R and the other...